Panagiotis Tsinganos holds a PhD degree (Doctor of Philophy and Doctor of Engineering Sciences) obtained from University of Patras, Greece and Vrije Universiteit Brussels, Belgium. He investigated the subject of gesture recognition based on surface electromyography (sEMG) using Deep Learning methods. His research interests include signal/image processing, machine learning and data mining as well as embedded systems programming.
Thesis title: «Multi-channel EMG pattern classification based on deep learning»
Thesis title: «Smartphone-based fall detection system for the elderly»
Grade: 9.02/10
Thesis title: «Transmission of biomedical signals using a wireless sensor network»
Grade: 8.31/10
P. Tsinganos, B. Jansen, J. Cornelis and A. Skodras, “Real-Time Analysis of Hand Gesture Recognition with Temporal Convolutional Networks”, Sensors, MDPI, 22(5), 1694, 2022.
P. Tsinganos, J. Cornelis, B. Cornelis, B. Jansen and A. Skodras, “Transfer Learning in sEMG-based Gesture Recognition”, 2021 International Conference on Information, Intelligence, Systems and Applications (IISA 2021), Chania, Greece, 2021.
P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras, “The Effect of Space-filling Curves on the Efficiency of Hand Gesture Recognition Based on sEMG Signals”, International Journal of Electrical and Computer Engineering Systems, 12(1), 2021.
P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras, “Data Augmentation of Surface Electromyography for Hand Gesture Recognition”, Sensors, MDPI, 20(17), 4892, 2020.
P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras, “Hilbert sEMG data scanning for hand gesture recognition based on deep learning”, Neural Computing and Applications, Springer, 2020.
P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras, “Hand Gesture Recognition Based on EMG Data: A Convolutional Neural Network Approach”, Physiological Computing Systems. PhyCS 2016, PhyCS 2017, PhyCS 2018. Lecture Notes in Computer Science, , A. Holzinger, A. Pope and H. Plácido da Silva, Springer, Cham, 2019, pp. 180-197.
P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras, “A Hilbert Curve Based Representation of sEMG Signals for Gesture Recognition”, 2019 International Conference on Systems, Signals and Image Processing (IWSSIP), Osijek, Croatia, 2019, pp. 201-206.
P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras, “Improved Gesture Recognition Based on sEMG Signals and TCN”, 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 2019, pp. 1169–1173.
P. Tsinganos, A. Skodras, B. Cornelis and B. Jansen, “Deep Learning in Gesture Recognition Based on sEMG Signals”, Learning Approaches in Signal Processing, 1st ed., F. Ring, W.-C. Siu, L.-P. Chau, L. Wang and T. Tang, Eds. Pan Stanford Publishing, 2018, pp. 471.
P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras, “Deep Learning in EMG-based Gesture Recognition”, 5th International Conference on Physiological Computing Systems (PhyCS), Seville, Spain, 2018, pp. 107–114.
P. Tsinganos and A. Skodras, “On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique Fall Detection”, Sensors, MDPI, 18(2), 592, 2018.
P. Tsinganos and A. Skodras, “A Smartphone-based Fall Detection System for the Elderly”, 10th International Symposium on Image and Signal Processing and Analysis (ISPA), Ljubljana, Slovenia, 2017, pp. 53-58.
Development of a serious game controlled by a surface electromyography (sEMG) interface for rehabilitation purposes
C/C++, Python, Android, Matlab, HTML, JavaScript
Image and Signal Processing, Biomedical Signals
Udacity AI for Healthcare Nanodegree, Data Mining, Artificial Neural Networks, Deep Learning
Udacity Full Stack Developer Nanodegree, Flask, React, REST API, Auth0
Experience in ARM Cortex M3 and RTOS
TCP/IP, GSM, MQTT
Awarded for the paper with title “A Hilbert Curve Based Representation of sEMG Signals for Gesture Recognition” presented in IWSSIP 2019 Osijek, Croatia.